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• W3035448973 abstract "Gaussian Process Regression and Kernel Ridge Regression are popular nonparametric regression approaches. Unfortunately, they suffer from high computational complexity rendering them inapplicable to the modern massive datasets. To that end a number of approximations have been suggested, some of them allowing for a distributed implementation. One of them is the divide and conquer approach, splitting the data into a number of partitions, obtaining the local estimates and finally averaging them. In this paper we suggest a novel computationally efficient fully data-driven algorithm, quantifying uncertainty of this method, yielding frequentist $L_2$-confidence bands. We rigorously demonstrate validity of the algorithm. Another contribution of the paper is a minimax-optimal high-probability bound for the averaged estimator, complementing and generalizing the known risk bounds." @default.
• W3035448973 created "2020-06-19" @default.
• W3035448973 creator A5040448099 @default.
• W3035448973 date "2019-12-13" @default.
• W3035448973 modified "2023-09-27" @default.
• W3035448973 title "Data-driven confidence bands for distributed nonparametric regression" @default.
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• W3035448973 hasPublicationYear "2019" @default.
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